Staff Software Engineer, AI Reliability Engineering

About the role

Anthropic is seeking talented and experienced Reliability Engineers, including Software Engineers and Systems Engineers with experience and interest in reliability, to join our team. We will be defining and achieving reliability metrics for all of Anthropic’s internal and external products and services. While significantly improving reliability for Anthropic’s services, we plan to use the developing capabilities of modern AI models to reengineer the way we work. This team will be a critical part of Anthropic’s mission to bring the capabilities of groundbreaking AI technologies to benefit humanity in a safe and reliable way.

Responsibilities:

• Develop appropriate Service Level Objectives for large language model serving and training systems, balancing availability/latency with development velocity.
• Design and implement monitoring systems including availability, latency and other salient metrics.
• Assist in the design and implementation of high-availability language model serving infrastructure capable of handling the needs of millions of external customers and high-traffic internal workloads.
• Develop and manage automated failover and recovery systems for model serving deployments across multiple regions and cloud providers.
• Lead incident response for critical AI services, ensuring rapid recovery and systematic improvements from each incident
• Build and maintain cost optimization systems for large-scale AI infrastructure, focusing on accelerator (GPU/TPU/Trainium) utilization and efficiency

You may be a good fit if you:

• Have extensive experience with distributed systems observability and monitoring at scale
• Understand the unique challenges of operating AI infrastructure, including model serving, batch inference, and training pipelines
• Have proven experience implementing and maintaining SLO/SLA frameworks for business-critical services
• Are comfortable working with both traditional metrics (latency, availability) and AI-specific metrics (model performance, training convergence)
• Have experience with chaos engineering and systematic resilience testing
• Can effectively bridge the gap between ML engineers and infrastructure teams
• Have excellent communication skills.

Strong candidates may also:

• Have experience operating large-scale model training infrastructure or serving infrastructure (>1000 GPUs)
• Have experience with one or more ML hardware accelerators (GPUs, TPUs, Trainium, e.g.)
• Understand ML-specific networking optimizations like RDMA and InfiniBand.
• Have expertise in AI-specific observability tools and frameworks
• Understand ML model deployment strategies and their reliability implications
• Have contributed to open-source infrastructure or ML tooling

Deadline to apply: None. Applications will be reviewed on a rolling basis.